Jason Fang - Projects

Website SQL Analysis

Using SQL to analyze website performance is a daily routine for data scientists. It's not necessary to always use machine learning and deep learning models to create predictive models. Solving business problems is the top task for us rather than creating models. In this project, I use an e-commerce website dataset to analyze some website problems such as traffic source, bounce rate and so on. Even though I'd like to create some predictive models in real life, I only use SQL in this project.

Tableau Showcase

Here are some of my Tableau examples. These worksheets and dashboards were published on Tableau Public and were embedded on my website. You can see interactive worksheets and dashboards here and try them by yourself.

Fintech App

This is a real fintech company that wants to provide its customers with a paid mobile app subscription that will allow them to track all of their finances in one place. To attract customers, the company releases a free version of their app with some main features unlocked. The purpose of this project is to identify which users will most likely not enroll in the paid product so that additional offers can be given to them. I used several models and finally got 100% accuracy. This is just an example of how I find out the company's potential customers. The techniques I used here could be applied in other companies as well.

Time Series ARIMA models

Time series forecasting is a crucial topic for data scientists. Autoregressive integrated moving average (ARIMA) is one of the famous linear statistical models for time series forecasting. It usually outperforms machine learning and deep learning models for one-step forecasting on univariate datasets. There are different variations of ARIMA. In this project, I use one type of ARIMA model which is SARIMAX, Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors. A restaurant visitors dataset has been used in this project.

Spark Spam Filter

Data scientists deal with big data by using Spark which is very powerful and user-friendly. Spark 2.0 has shifted towards a dataframe syntax which is just like pandas and Spark's MLlib library is similar to sklearn library. You also can write SQL queries by using Spark. Since I've had many machine learning and SQL projects, I used Spark MLlib to create a spam filter in this Natural Language Processing project.

Website SQL Analysis

Tableau Showcase

Fintech App

Time Series ARIMA models

Spark Spam Filter

Let's work together on your next project